Cerebellar learning for control of a two-link arm in muscle space

Andrew H. Fagg*, Nathan Sitkoff, Andrew G. Barto, James C. Houk

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. In this paper, we present a model of cerebellar control of a muscle-actuated, two-link, planar arm. The model learns in a trial-and-error fashion to produce bursts of muscle activity that accurately bring the arm to a specified target. When the cerebellum fails to bring the arm to the target, an extra-cerebellar module performs low-quality corrective movement, from which the cerebellum may update its program. In learning to perform the task, the cerebellum constructs an implicit inverse model of the plant. This model uses a combination of delayed sensory signals and recently-generated motor commands to compute the new output motor signal.

Original languageEnglish (US)
Pages (from-to)2638-2644
Number of pages7
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume3
StatePublished - Jan 1 1997

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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